Comprehending the theoretical knowledge and practice around AI-enabled innovations in the finance sector

Abstract

This study adopts a comprehending theory (CT) approach towards understanding machine learning (ML) for theory and practice within the finance sector. In building on prior research, the study explores the hidden meanings of ML phenomena and connects them to the underlying financial motivation behind the actions of financial firms to create greater intellectual insight for users in practice. At its most basic, the study explores why the meaning and conception of ML is confusing and ambivalent for users in the sector. Through a scoping review, only top-tier quartile one publications between the years of 2014 to 2024 were chosen for the review with 167 articles selected for analysis. In making a significant contribution to theory, a classification framework was developed to provide greater meaning and clarification of different ML criteria. The study matches relevant CT criteria with the opportunities and challenges of ML identifying significant differences between theory and practice. The study thus substantially contributes to broadening and extending existing knowledge related to ML in the financial sector by better explaining what these gaps look like and what to do about them for future research.